Pybaobab – Python implementation of visualization technique for decision trees

And the GitLab repo: https://gitlab.tue.nl/20040367/pybaobab

Its not that decision trees are under utilized, but rather one of their disadvantages is that they can overfit to their training data very easily. This is actually one of the reasons Random Forests exist - just make a lot of overfitted decision trees and then vote for consensus.

The visualization looks great, though it did suffer visualizing the Random Forest. I could see using it for a single Decision Tree to convey the data's structure. Definitely going to use it for any DT slides I have to make.

People can also follow additive scores. This is what e.g. the DSM does.

A spreadsheet-like printed table where you mark items and sum scores in your head is probably easier to follow than a similarly-powered decision tree. Of course, you can't guarantee that a linear decision boundary exists, but in case there is one, the standard tools (Gauss-Markov/FWL theorems, p-values, etc.) are much, much more robust than CART or C4.5.

Agreed... but the decision trees we learn from data to do classification are quite different from the decision trees we design to formalise our processes. I think several replies here have missed the distinction.

I had a weird experience recently. We tore out our deck during Covid lockdown and I had a rusty nail puncture my skin. The doctor who treated it pulled out a sheet that was essentially a decision tree that determined that after cleaning the wound I just needed to be sent home with a lollipop. (Recent tetanus shot was sufficient.)

The strange part was when the doctor showed me the tree and asked me if I agreed... My response was "You're the doctor, you tell me?!"

Not really. If we are doing data science for many companies and explainability is an aspect we will likely go for decision trees if the lift for advanced models is minor anyways. We use this (not quite as pretty for visualization but extremely useful to get a grasp if the tree model): https://github.com/parrt/dtreeviz

I have been taught that the proper way is to use an algorithm that will summarize the random forest as a close-enough decision tree. I have also been taught that it can be very misleading...

A mindmapper (e.g. XMind) could be (ab)used for that.